Solo @projectsolo
Redefining Web3 Trust with AI | backed by @drapervc @velocitycap_ projectsolo.xyz Joined August 2024-
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Deloitte projects AI agents will exceed human oversight capacity by 2027. 74% of enterprises will be running more agents than humans can meaningfully supervise. The oversight model isn't broken. It's being asked to scale past its architectural limits.
LinkedIn removed 40 million fake accounts in 6 months. That was before generative AI could fake profiles, work histories, and voice verification. The synthetic identity problem in 2026 is a different magnitude. Detection doesn't scale. Verification does.
@Liquify_ltd @wormhole Awesome news! Congrats to both teams 🤝
As AI agents become indistinguishable from humans online, identity becomes less important than humanity. The next digital economy won’t ask: “Who are you?” It will ask: “Are you human?” Proof-of-humanity is becoming the foundation of trust in the age of AI.
Experian confirmed it: synthetic job candidates are landing enterprise interviews. AI-generated faces. Cloned voices. Fabricated credentials. Hiring managers can't tell the difference on a video call. The fix isn't detection software. It's verification before the call starts.
The new deepfake attack surface isn't external. It's your contact center. Your internal comms. Your account recovery process. The attacker is already inside the trust boundary. Detection at the perimeter was never the fix.
Bloomberg just published: AI is making fraud easier, faster, and harder to stop simultaneously. Not one of those things. All three at once. Every fraud team building detection tools is fighting a losing race. You can't detect your way out of an identity problem.
Enterprises are spending over $1 million cleaning up after compromised AI agents. Detection takes hours. The damage is already done. The root cause is the same every time: no verified human at the authorization endpoint. Fix the foundation.
If an AI model was trained on data labeled by bots, would you trust its outputs?
Ping Identity: only 9% of companies are prepared for AI-powered identity threats. The threat they're not ready for: rogue agents that look exactly like legitimate ones. Your defense stack can't tell them apart. The attack surface is the authorization layer.
Gartner forecasts 40% of enterprise applications will feature AI agents by the end of 2026. Only 6% of organizations have an advanced AI security strategy. The deployment curve & the security curve are diverging in real time. That gap is the next wave of enterprise breaches.
The FTC recorded $12.5 billion in consumer fraud losses in 2024. Experian's 2026 forecast: agentic AI inflates that baseline rapidly. Every case in their report starts the same way. A system assumed there was a human on the other side.
"Privacy without identity is just hiding" said DeFi.com CEO Neil May. The ZK ecosystem built world-class tools for hiding transactions. Nobody built the same infrastructure for verifying the human behind them. Different problem. Different architecture.
Researchers just published a new class of LLM agent attack: response-path attacks. The model is perfectly aligned. The safety training holds. The attack works anyway. Alignment was never the verification layer.
GM to humans! Bots and AI never sleep. What keeps you grounded each morning?
Somewhere in your enterprise, an AI agent is running right now that nobody owns. The developer moved on. The project is over. The agent is still there. Credentialed. Accessing your systems. No alert ever fired. This is the zombie agent problem.
Two-thirds of enterprises suspect their AI agents have already accessed unauthorized data. Most couldn't detect it for hours. The agent had valid credentials the whole time. The authorization chain had no verified human at the top.
Anthropic just built a KYC agent for banks. It automates document screening, AML checks, onboarding flows. KYC verifies documents. Not people. Automating a broken foundation faster is not a fix.
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